D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 41 Citations 8,046 225 World Ranking 4318 National Ranking 2169

Research.com Recognitions

Awards & Achievements

2007 - Fellow of the American Association for the Advancement of Science (AAAS)

2006 - IEEE Fellow For contributions to statistical methodologies for performance assessment of computing systems.

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Central processing unit
  • Programming language

His scientific interests lie mostly in Parallel computing, Computer architecture, Benchmark, Stochastic computing and Algorithm. His research in Parallel computing intersects with topics in Fault tolerance, Compiler and Set. The concepts of his Computer architecture study are interwoven with issues in Range, Software system and Flexibility.

His Benchmark study incorporates themes from Spec# and Theoretical computer science. His Stochastic computing study integrates concerns from other disciplines, such as Stochastic process, Digital image processing, Binary number, Logic gate and Finite-state machine. His Algorithm research incorporates elements of Central processing unit, Graphics hardware and Computational science.

His most cited work include:

  • MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research (327 citations)
  • Measuring computer performance : A practitioner's guide (313 citations)
  • Data prefetch mechanisms (274 citations)

What are the main themes of his work throughout his whole career to date?

His primary scientific interests are in Parallel computing, Algorithm, Stochastic computing, Computer architecture and Distributed computing. His works in Multiprocessing, Cache, Speedup, Shared memory and Cache pollution are all subjects of inquiry into Parallel computing. His Algorithm course of study focuses on Set and Benchmark and Data mining.

His Stochastic computing research is multidisciplinary, incorporating elements of Image processing, Computer engineering, Logic gate and Finite-state machine. The Computer architecture study combines topics in areas such as Instruction set, Microarchitecture and Verilog. His biological study spans a wide range of topics, including Telecommunications network, Scheduling, Computer network and Dynamic priority scheduling.

He most often published in these fields:

  • Parallel computing (30.03%)
  • Algorithm (14.19%)
  • Stochastic computing (11.55%)

What were the highlights of his more recent work (between 2014-2021)?

  • Stochastic computing (11.55%)
  • Algorithm (14.19%)
  • Artificial neural network (4.29%)

In recent papers he was focusing on the following fields of study:

His primary areas of investigation include Stochastic computing, Algorithm, Artificial neural network, Parallel computing and Computer engineering. His research integrates issues of Energy consumption, Logic gate, Binary number and Image processing in his study of Stochastic computing. His study looks at the relationship between Algorithm and topics such as Set, which overlap with Mathematical optimization.

His Artificial neural network research is multidisciplinary, incorporating perspectives in Field-programmable gate array, Computer hardware and Reduction. He studies Parallel computing, namely Degree of parallelism. His Computer engineering research incorporates themes from JPEG, Huffman coding, Overhead and Theoretical computer science.

Between 2014 and 2021, his most popular works were:

  • Approximate Communication: Techniques for Reducing Communication Bottlenecks in Large-Scale Parallel Systems (70 citations)
  • A hardware implementation of a radial basis function neural network using stochastic logic (45 citations)
  • Using Stochastic Computing to Reduce the Hardware Requirements for a Restricted Boltzmann Machine Classifier (43 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Central processing unit
  • Programming language

David J. Lilja mainly focuses on Stochastic computing, Artificial neural network, Logic gate, Algorithm and Computation. His Stochastic computing research includes elements of Image processing, Finite-state machine, Binary number and Energy consumption. He combines subjects such as Field-programmable gate array, Computer hardware and Adder with his study of Artificial neural network.

In general Algorithm, his work in Non-negative least squares is often linked to Exponential function, Inversion and Active set method linking many areas of study. His Software research includes themes of Speedup and Parallel computing. Parallel computing is frequently linked to Skew in his study.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Measuring computer performance : A practitioner's guide

David J. Lilja.
(2000)

701 Citations

MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research

A.J. KleinOsowski;D.J. Lilja.
IEEE Computer Architecture Letters (2002)

487 Citations

Data prefetch mechanisms

Steven P. Vanderwiel;David J. Lilja.
ACM Computing Surveys (2000)

422 Citations

An Architecture for Fault-Tolerant Computation with Stochastic Logic

Weikang Qian;Xin Li;M D Riedel;K Bazargan.
IEEE Transactions on Computers (2011)

354 Citations

BloomFlash: Bloom Filter on Flash-Based Storage

Biplob Debnath;Sudipta Sengupta;Jin Li;David J. Lilja.
international conference on distributed computing systems (2011)

345 Citations

The superthreaded processor architecture

Jenn-Yuan Tsai;Jian Huang;C. Amlo;D.J. Lilja.
IEEE Transactions on Computers (1999)

248 Citations

Cache coherence in large-scale shared-memory multiprocessors: issues and comparisons

David J. Lilja.
ACM Computing Surveys (1993)

200 Citations

A statistically rigorous approach for improving simulation methodology

J.J. Yi;D.J. Lilja;D.M. Hawkins.
high-performance computer architecture (2003)

194 Citations

Accelerating Lattice Boltzmann Fluid Flow Simulations Using Graphics Processors

Peter Bailey;Joe Myre;Stuart D.C. Walsh;David J. Lilja.
international conference on parallel processing (2009)

182 Citations

Computation on Stochastic Bit Streams Digital Image Processing Case Studies

Peng Li;David J. Lilja;Weikang Qian;Kia Bazargan.
IEEE Transactions on Very Large Scale Integration Systems (2014)

170 Citations

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